Computer aided detection of prostate cancer on biparametric MRI using a quadratic discriminant model

Abstract

This paper presents a computer-aided detection (CAD) algorithm for detection of prostate cancer (PCa) in biparametric magnetic resonance imaging (bpMRI). Using image intensity, gradient and gradient direction from T2-weighted (T2 W), diffusion weighted imaging (DWI) and apparent diffusion coefficient (ADC) MRI series, together with a distance feature, a quadratic discriminant analysis (QDA) model was evaluated in 18 patients. A 3D probability map was created for each patient and the number of true- and false positive tumors was determined. Visual assessment showed that for the majority of patients, highest tumor probability was found within the expert annotated volume. The algorithm successfully located 21 of 22 tumors with 0 to 4 false positive per patient. However, the algorithm had a tendency of under-estimating the tumor volume compared to the expert. The study suggests that features extracted from bpMRI can be used for automatic detection of PCa with performance comparable to existing CAD algorithms.